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A Neural Tactile Architecture Applied to Real-time Stiffness Estimation for a Large Scale of Robotic Grasping Systems

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Abstract

This paper presents a model for solving the problem of real-time neural estimation of stiffness characteristics for unknown objects. For that, an original neural architecture is proposed for a large scale robotic grasping systems applied for unknown object with unspecified stiffness characteristics. The force acquisition is based on tactile information from force sensors in robotic manipulator. The proposed model has been implemented on a robotic gripper with two parallel fingers and on a one d.o.f. robotic finger with opponent artificial muscles and angular displacements. This self-organized model is inspired of human biological system, and is carried out by means of Topographic Maps and Vector Associative Maps. Experimental results demonstrate the efficiency of this new approach.

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Authors and Affiliations

  1. Department of Information Technologies and Communications, Polytechnical University of Cartagena, Cartagena, 30202, Murcia, Spain

    J. L. Pedreño-Molina

  2. Department of System Engineering and Automatic, Polytechnical University of Cartagena, Cartagena, 30202, Murcia, Spain

    A. Guerrero-González & J. López-Coronado

  3. Department of Electrical and Electronics Engineering, University of Leon, Avenida de Portugal 41, 24071, Leon, Spain

    J. Calabozo-Moran

  4. Handibio-LESP EA 3162, University of the South Toulon-Var, BP 20132, 83957, La Garde Cedex, France

    P. Gorce

Authors
  1. J. L. Pedreño-Molina
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  2. A. Guerrero-González
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  3. J. Calabozo-Moran
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  4. J. López-Coronado
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  5. P. Gorce
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Correspondence to P. Gorce.

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Pedreño-Molina, J.L., Guerrero-González, A., Calabozo-Moran, J. et al. A Neural Tactile Architecture Applied to Real-time Stiffness Estimation for a Large Scale of Robotic Grasping Systems. J Intell Robot Syst 49, 311–323 (2007). https://doi.org/10.1007/s10846-006-9040-x

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  • DOI: https://doi.org/10.1007/s10846-006-9040-x

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